LLM Content Focus: What ChatGPT, Perplexity, and Gemini Prefer

Standard search engine optimization was yesterday – today it's also about designing content in such a way that it can be found, understood, and integrated into responses by Large Language Models (LLMs) like ChatGPT, Perplexity, and Google Gemini. 
Being cited as a source in AI-generated results not only benefits brand awareness but often also provides valuable backlinks. 
However, each LLM has its own focus when it comes to selecting content. In this article, you'll learn how these three models work and how you can tailor your content to their preferences. 

An Overview of the Three LLMs 

Before we delve into specific tactics, it's worth taking a quick look at how the models function. Each LLM evaluates content according to its own criteria. ChatGPT scores highly with well-structured explanations, Perplexity prioritizes recency and sources, and Gemini uses strong signals from the Google Index and prefers structured and multimedia content. These differences determine which content you should prioritize. 

ChatGPT – Creative & Dialogical Content

 ChatGPT excels at reproducing content in a natural, human-sounding language. It favors text that is easy to read, provides clear explanations, and is organized in a logical structure. 

• Preferred Content: Storytelling, illustrative examples, step-by-step explanations Style: dialogical, accessible, understandable to a wide audience

• Data Source: Mainly training data, with web access in the Pro version 

• Success Factor: Evergreen content that is mentioned on many trustworthy sites has a better chance of being included in the model  

  
  

Perplexity – Research, Sources, Recency 

Perplexity is an LLM with integrated real-time web access. The unique aspect: It always shows sources and directly links to them. 

• Preferred Content: Recent studies, statistics, professional articles, precise analyses Style: factual, evidence-based, concise 

• Data Source: Live internet search + structured sources 

• Success Factor: Clear source citations, publication date, author, imprint – and content that directly addresses the question posed 

• Extra Tip: FAQ formats and How-To guides are particularly visible because Perplexity often presents answers in a Q&A structure  
  
  

Google Gemini – Multimodal & SEO-Driven 

Gemini is closely linked with the Google ecosystem and uses traditional search data to integrate content into AI responses. Additionally, it can combine text, image, video, and audio. 

• Preferred Content: SEO-optimized articles, rich snippets, structured data (Schema.org) 

• Style: informative, well-organized, with visual elements such as infographics or tables 

• Data Source: Google Search Index + multimodal analysis 

• Success Factor: Content that already performs well in organic Google ranking has a significantly better chance of appearing in Gemini  

Content Priorities in Direct Comparison 

There are significant differences between the models. ChatGPT prefers reader-friendly explanations, Perplexity demands recency and sources, and Gemini rewards SEO structure and media diversity. Use this matrix as a guide for your editorial plan.   

Optimization Strategies for Each LLM 

Even though best practices overlap, focusing on the specific preferences of the models is worthwhile. This way, you can garner more mentions and links. 

Optimize for ChatGPT 

Begin each key section with the most important answer, followed by brief justifications and at least one example. Explain technical terms in your own words, add a concise definition, and link to additional internal pages if necessary.  

Structure is crucial. Use clear H2 and H3, frame frequent user questions as subheadings, and answer them directly in the first paragraph below. Add practical examples, checklists, and small sequences of steps. This increases the chance that passages will be used as complete answers.  

Optimize for Perplexity 

Build a clean source concept. Name primary sources, use quotes sparingly but precisely, and provide numbers with links and dates. Insert a brief summary with three to five key statements at the beginning of an article.  Make the publication date, author, and company information clearly visible. Regularly update content.  
Maintain an FAQ block with real user questions and concise answers of 40 to 80 words. Include tables with important metrics. This increases the likelihood of being directly linked. Additionally, you can bundle in-depth resources and provide them in a resources section at the end.  

Optimize for Gemini 

Focus on proper on-page fundamentals. Optimize title and meta description, establish a clear heading hierarchy, and use Schema.org markup.  Build internal links with descriptive anchor text to thematically related pages, such as guide articles or service pages. Create media to foster understanding, such as an infographic with process steps or a table with pros and cons. Pay attention to E-E-A-T signals. An author profile with qualifications, references, and contact information builds trust.  

Examples of Content Elements that LLMs Favor 

• Short definition at the beginning, maximum two sentences, directly related to the question. 

• Explanation section with a real-life example.  

• Mini checklist with three to five points that makes a task doable. 

•  Table with criteria, such as comparison of methods, costs, or risks. 

•  FAQ section with three to seven real questions. 

These building blocks can be used in blog posts, service pages, and knowledge articles. In online shops, they can also function as supplementary guides on category pages.  

Common Mistakes That Prevent Mentions 

One of the most common mistakes is an unclear structure where users cannot find a direct answer at the beginning of a section immediately. A lack of sources or the use of outdated data also negatively impacts credibility.  If a topic is too broadly covered on just a single page, relevance decreases along with the chance of being mentioned. Furthermore, the absence of publication date and author leads to less trust in the content. Likewise, a lack of internal linking can result in crucial context signals being missed, causing LLMs not to rate the content as particularly relevant. 
To avoid these hurdles, you should regularly review existing content, structure it carefully, and update it specifically. 

Conclusion 

Optimization for LLMs is not futuristic – it is already crucial to remain visible in the new search world. ChatGPT prefers easily understandable, creative, and well-explained content Perplexity relies on current, evidence-based, and source-supported content Gemini accesses SEO-strong, structured, and multimedia content The requirements of ChatGPT, Perplexity, and Gemini vary – but with the right strategy, you can excel in all three models. We support you in developing content that is found, mentioned, and linked not only by search engines but also by AI systems. Get in touch now.   

FAQ – Frequently Asked Questions About Content Focus 

How do I know if my content is mentioned in LLMs? 
With Perplexity, it's easy – sources are linked. With ChatGPT and Gemini, you can test this through targeted queries or monitor it with tracking tools. 

Do I need to optimize separately for each LLM? 
Yes, as the models have different focuses. However, there are overlaps, e.g., with clear structure and high source quality. 

How often should I update content? 
For Perplexity and Gemini regularly, as recency is a crucial factor. Evergreen content for ChatGPT should also be maintained.